Artificial Intelligence, Machine Learning, and Data Science (AI/ML/DS) Technical Support Services

The Department of Homeland Security Science and Technology Directorate (DHS S&T) requires professional scientific, technical and programmatic assistance services to support development of advanced passenger and baggage screening technologies and automated image analysis software for the Transportation Security Administration (TSA).

TSA projects passenger screening throughput will increase over the next decade from 3 million to 10 million passengers per day, which will require new and advanced passenger and baggage screening technologies to handle these projected loads while simultaneously improving passenger experience.

The overall intent of this technical effort is to provide artificial intelligence, machine learning, and data science expertise and experience for research, development, and assessment of technologies for improving the security, throughput, and passenger experience at airport checkpoints and similar environments.

The specific intent of this technical effort is to apply artificial intelligence, machine learning (AIML), and data science (DS) expertise and experience towards supporting, developing, and delivering various AIML and DS tools to improve checkpoint screening throughput and related equities.

This includes conducting and providing independent evaluations, supporting testing, validation, and standards development, evaluating and analyzing AIML and DS tools, developing and evaluating automated image analysis software and algorithms, supporting development and interpretation of open architecture data and metadata standards, and supporting and creating algorithm training, validation, testing and assurance tools for advanced screening and related technologies. The purposes of this RFI are:

To gain an understanding of the capabilities of small businesses to support DHS S&T’s AI/ML/DS requirements. To gain an understanding of Industry’s capabilities to support DHS S&T’s AI/ML/DS requirements. Conduct market research for a potential task order under the GSA OASIS+ contract vehicle.